Metadata-Version: 2.1
Name: DACOpt
Version: 0.0.2
Summary: DACOpt: An Efficient Contesting Procedure for AutoML Optimization
Home-page: UNKNOWN
Author: Duc Anh Nguyen
Author-email: d.a.nguyen@liacs.leidenuniv.nl
License: GPL-3.0 License
Keywords: AutoML optimization Divide and conquer Bayesian Optimization
Platform: UNKNOWN
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: LICENSE.txt

#DACOpt: An Efficient Contesting Procedure for AutoML Optimization



### Contact us



Duc Anh Nguyen



Email:d-dot-a-dot-nguyen-at-liacs-dot-leidenuniv-dot-nl



Website: [ecole-itn.eu](https://ecole-itn.eu/)

## Installation

### Requirements



As requirements  mentioned in `requirements.txt`, [hyperopt](https://github.com/hyperopt/hyperopt) and [BO4ML](https://github.com/ECOLE-ITN/NguyenSSCI2021) as build dependencies:



```shell

pip install hyperopt

pip install BO4ML

```

### Installation



You could either install the stable version on `pypi`:



```shell

pip install DACOpt

```



Or, take the lastest version from github:



```shell

pip install git+https://github.com/ECOLE-ITN/DACOpt.git

```

--

```shell

git clone https://github.com/ECOLE-ITN/DACOpt.git

cd DACOpt && python setup.py install --user

```



## Example



See our [experiments folder](https://github.com/ECOLE-ITN/DACOpt/DACOpt_experiment)



## Citation

### Paper Reference



Duc Anh Nguyen, Anna V. Kononova, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck. An Efficient Contesting Procedure for AutoML Optimization. TBA



## Acknowledgment



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 766186 (ECOLE).



